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Natural Language Processing(NLP) with Deep Learning in Keras

Word2Vec, Glove, FastText, Universal Sentence Encoder, GRU, LSTM, Conv-1D, Seq2Seq, Machine Translation and much more!
Instructor:
CARLOS QUIROS
219 students enrolled
English [Auto-generated]
Upgrade the knowledge of Natural Language Processing using Deep Learning models

Natural Language Processing (NLP) is a hot topic into Machine Learning field. 

This course is an advanced course of NLP using Deep Learning approach. Before starting this course please read the guidelines of the lesson 2 to have the best experience in this course.

This course starts with the configuration and the installation of all resources needed including the installation of Tensor Flow CPU/GPU, Cuda and Keras. You will be able to use your GPU card if you have one, to accelate so fast the processes. But if you dont have a GPU card you can follow the instructions for running the standard CPU code, it will take a while but you still can run it.

After that we are going to review the main concepts of Deep Learning in the Chapter 2 for applying them into the Natural Language Processing field offering you a solid background for the main chapter.

In the main Chapter 3 we are going to study the main Deep Learning libraries and models for NLP such as Word Embeddings, Word2Vec, Glove, FastText, Universal Sentence Encoder, RNN, GRU, LSTM, Convolutions in 1D, Seq2Seq, Memory Networks, and the Attention mechanism.

This course offers you many examples, with different datasets suchs as Google News, Yelp comments, Amazon reviews, IMDB reviews, the Bible corpus, etc and different text corpus. At the final in Chapter 4 you will put in practice your knowledge with practical applications such as Multiclass Sentiment Analysis, Text Generation, Machine Translation, Developing a ChatBot and more. 

For coding we are going to use TensorFlow, Keras, Google Colab and many Python libraries.

If you need a previous background in Natural Language Processing or in Machine Learning I recommend you my courses:

  • Python for Machine Learning and Data Mining  or 

  • Natural Language Processing with Python and NLTK

Installation and Setup

1
Introduction
2
Before starting read these important guidelines!!
3
Resources: Codes, Datasets, Papers and more
4
Package Installation and Setup
5
Installing TensorFlow for CPU/GPU/Cuda and Keras

Deep Learning Overview

1
Deep Learning Concept
2
Gradient Descent
3
Vanishing and Exploding Gradients
4
Beyond Gradient Descent
5
Loss Functions
6
Activation Functions
7
Dropout
8
Batch Normalization
9
Convolutional Layers

Deep Learning for NLP

1
Introduction
2
Word Embeddings on Sentiment Analysis
3
Visualizing Word Embeddings
4
Word2Vec Model
5
Word2Vec-Skipgrams-Keras
6
Word2Vec-CBOW-Keras
7
The GloVe model
8
Yelp Comments Classification with GloVe part 1
9
Yelp Comments Classification with GloVe part 2
10
FastText model
11
FastText with Gensim
12
FastText on Google Collaboratory
13
RNN-GRU-LSTM models
14
Sentiment Analysis with GRU, CuDNNGRU, LSTM, CuDNNLSTM-part 1
15
Sentiment Analysis with GRU, CuDNNGRU, LSTM, CuDNNLSTM-part 2
16
1D Convolutional Layer for NLP
17
Sentiment Analysis with 1D ConvNet part 1
18
Sentiment Analysis with 1D ConvNet part 2
19
Seq2Seq and the Attention Mechanism
20
Encoder-Decoder with Attention
21
Universal Sentence Encoder
22
Semantic Similarity with TF-Hub Universal Encoder
23
Text Classification with TF-Hub Universal Encoder

Applications

1
Text Generation
2
Emotion recognition with LSTM and Attention part 2
3
Emotion recognition with LSTM and Attention part 1
4
Neural Machine Translation with Seq2Seq
5
Improving Neural Machine Translation part 2
6
Improving Neural Machine Translation part 1
7
Developing a Chat Bot part 1
8
Developing a Chat Bot part 2
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8 hours on-demand video
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Certificate of Completion